#202252320421
#罗坤帅

# 导入必要的库和模块
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import pickle
import matplotlib.pyplot as plt

# 加载数字数据集
digits = datasets.load_digits()

# 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)

# 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn = None

# 初始化一个列表以存储每个k值的准确率
accuracy_list = []

# 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in range(1, 41):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    accuracy_list.append(accuracy)
    
    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn = knn

# 将最佳KNN模型保存到二进制文件
with open('best_knn_model.pkl', 'wb') as file:
    pickle.dump(best_knn, file)

# 打印最佳准确率和相应的k值
print(f"Best accuracy: {best_accuracy} with k: {best_k}")

# 可视化不同k值的准确率
plt.figure(figsize=(10, 6))
plt.title('Accuracy vs. K Value')
plt.plot(range(1, 41), accuracy_list, marker='o')
plt.xlabel('K Value')
plt.ylabel('Accuracy')
plt.show()